Analysis of Financial Time Series

Preface. Preface to First Edition. 1. Financial Time Series and Their Characteristics. 2. Linear Time Series Analysis and Its Applications. 3. Conditional Heteroscedastic Models. 4. Nonlinear Models and Their Applications. 5. High-Frequency Data Analysis and Market Microstructure. 6. Continuous-Time Models and Their Applications. 7. Extreme Values, Quantile Estimation, and Value at Risk. 8. Multivariate Time Series Analysis and Its Applications. 9. Principal Component Analysis and Factor Models. 10. Multivariate Volatility Models and Their Applications. 11. State-Space Models and Kalman Filter. 12. Markov Chain Monte Carlo Methods with Applications. Index.

[1]  P. Lewis,et al.  Nonlinear Modeling of Time Series Using Multivariate Adaptive Regression Splines (MARS) , 1991 .

[2]  C. Granger,et al.  Co-integration and error correction: representation, estimation and testing , 1987 .

[3]  Timothy G. Conley,et al.  Short-term interest rates as subordinated diffusions , 1997 .

[4]  D. Duffie,et al.  An Overview of Value at Risk , 1997 .

[5]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[6]  F. Black,et al.  The Pricing of Options and Corporate Liabilities , 1973, Journal of Political Economy.

[7]  Michael P. Clements,et al.  On the limitations of comparing mean square forecast errors , 1993 .

[8]  James D. Hamilton.  Analysis of time series subject to changes in regime , 1990 .

[9]  James D. Hamilton.  A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle , 1989 .

[10]  R. Tsay.  Outliers, Level Shifts, and Variance Changes in Time Series , 1988 .

[11]  M. Tanner.  Tools for statistical inference: methods for the exploration of posterior distributions and likeliho , 1994 .

[12]  W. Fuller,et al.  Distribution of the Estimators for Autoregressive Time Series with a Unit Root , 1979 .

[13]  Eric Ghysels,et al.  Some Econometric Recipes for High-Frequency Data Cooking , 2000 .

[14]  B. LeBaron,et al.  A test for independence based on the correlation dimension , 1996 .

[15]  Yacine Ait-Sahalia.  Maximum-Likelihood Estimation of Discretely Sampled Diffusions: A Closed-Form Approach , 1998 .

[16]  Mathieu Kessler.  Estimation of an Ergodic Diffusion from Discrete Observations , 1997 .

[17]  Chris Chatfield,et al.  Introduction to Statistical Time Series. , 1976 .

[18]  David Hsieh.  Testing for Nonlinear Dependence in Daily Foreign Exchange Rates , 1989 .

[19]  Patrick Billingsley,et al.  Probability and Measure. , 1986 .

[20]  Hong Chen,et al.  Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems , 1995, IEEE Trans. Neural Networks.

[21]  H. Tong.  Non-linear time series. A dynamical system approach , 1990 .

[22]  D. Heming.  Fourth Generation Management: The New Business Consciousness , 1994 .

[23]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[24]  A. Ronald Gallant,et al.  The relative efficiency of method of moments estimators , 1999 .

[25]  Sidney I. Resnick,et al.  Extremal behaviour of solutions to a stochastic difference equation with applications to arch processes , 1989 .

[26]  Jón Dańıelsson,et al.  Tail Index and Quantile Estimation with Very High Frequency Data , 1997 .

[27]  Y. Tse,et al.  A Multivariate GARCH Model with Time-Varying Correlations , 2000 .

[28]  Cathy W. S. Chen,et al.  On a threshold heteroscedastic model , 2006 .

[29]  J. R. M. Hosking,et al.  The Multivariate Portmanteau Statistic , 1980 .

[30]  T. Rao,et al.  An Introduction to Bispectral Analysis and Bilinear Time Series Models , 1984 .

[31]  Michael L. George,et al.  What is Lean Six Sigma , 2003 .

[32]  G. C. Tiao,et al.  Journal of the American Statistical Association Forecasting the U.s. Unemployment Rate Forecasting the U.s. Unemployment Rate , 2022 .

[33]  Guyonne Kalb,et al.  Bayesian Arbitrage Threshold Analysis , 1999 .

[34]  Christian M. Dahl,et al.  Specifying Nonlinear Econometric Models by Flexible Regression Models and Relative Forecast Performance , 1999 .

[35]  Peter J. Brockwell,et al.  On the general bilinear time series model , 1988 .

[36]  N. Metropolis,et al.  The Monte Carlo method. , 1949 .

[37]  Nicholas G. Polson,et al.  A Monte Carlo Approach to Nonnormal and Nonlinear State-Space Modeling , 1992 .

[38]  R. Roll,et al.  A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market , 2008 .

[39]  Richard H. Jones,et al.  Maximum Likelihood Fitting of ARMA Models to Time Series With Missing Observations , 1980 .

[40]  W. Härdle.  Applied Nonparametric Regression , 1991 .

[41]  Ruey S. Tsay,et al.  On the Ergodicity of Tar(1) Processes , 1991 .

[42]  F. Longin,et al.  From value at risk to stress testing : The extreme value approach Franc ß ois , 2000 .

[43]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[44]  Steven G. Kou,et al.  A jump diffusion model for option pricing with three properties: leptokurtic feature, volatility smile, and analytical tractability , 2000, Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520).

[45]  G. C. Tiao,et al.  A canonical analysis of multiple time series , 1977 .

[46]  T. Teräsvirta.  Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models , 1994 .

[47]  H. Stoll,et al.  Stock Market Structure and Volatility , 1990 .

[48]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[49]  L. Tierney.  Markov Chains for Exploring Posterior Distributions , 1994 .

[50]  B. M. Hill,et al.  A Simple General Approach to Inference About the Tail of a Distribution , 1975 .

[51]  A. Lo,et al.  An Ordered Probit Analysis of Transaction Stock Prices , 1991 .

[52]  Joel Hasbrouck,et al.  The Dynamics of Discrete Bid and Ask Quotes , 1996 .

[53]  Jon Danielsson,et al.  Value-at-Risk and Extreme Returns , 2000 .

[54]  J. R. M. Hosking,et al.  Lagrange‐Multiplier Tests of Multivariate Time‐Series Models , 1981 .

[55]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[56]  Steven C. Hillmer,et al.  Likelihood Function of Stationary Multiple Autoregressive Moving Average Models , 1979 .

[57]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[58]  Simon M. Potter.  A Nonlinear Approach to U.S. GNP , 1993 .

[59]  R. McCulloch,et al.  BAYESIAN ANALYSIS OF AUTOREGRESSIVE TIME SERIES VIA THE GIBBS SAMPLER , 1994 .

[60]  Ruey S. Tsay,et al.  Testing and Modeling Threshold Autoregressive Processes , 1989 .

[61]  R. Davies.  Hypothesis testing when a nuisance parameter is present only under the alternative , 1977 .

[62]  Ruey S. Tsay,et al.  A nonlinear autoregressive conditional duration model with applications to financial transaction data , 2001 .

[63]  N. Shephard,et al.  Multivariate stochastic variance models , 1994 .

[64]  P. Phillips.  Time series regression with a unit root , 1987 .

[65]  R. Tsay.  Testing and modeling multivariate threshold models , 1998 .

[66]  Peter E. Rossi,et al.  Bayesian Analysis of Stochastic Volatility Models , 1994 .

[67]  G. C. Tiao,et al.  Estimation of time series parameters in the presence of outliers , 1988 .

[68]  Adrian F. M. Smith,et al.  Sampling-Based Approaches to Calculating Marginal Densities , 1990 .

[69]  R. Tsay.  Nonlinearity tests for time series , 1986 .

[70]  Timo Teräsvirta,et al.  Testing linearity against smooth transition autoregressive models , 1988 .

[71]  S. Berman.  Limit Theorems for the Maximum Term in Stationary Sequences , 1964 .

[72]  W. Wong,et al.  The calculation of posterior distributions by data augmentation , 1987 .

[73]  J. Petruccelli,et al.  A threshold AR(1) model , 1984, Journal of Applied Probability.

[74]  G. C. Tiao,et al.  Asymptotic properties of multivariate nonstationary processes with applications to autoregressions , 1990 .

[75]  G. Kitagawa.  A self-organizing state-space model , 1998 .

[76]  F. Longin.  Optimal margin level in futures markets: Extreme price movements , 1999 .

[77]  J. Stock,et al.  Testing for Common Trends , 1988 .

[78]  Kurt Hornik,et al.  Some new results on neural network approximation , 1993, Neural Networks.

[79]  G. C. Tiao,et al.  Modeling Multiple Time Series with Applications , 1981 .

[80]  M. Hinich.  Testing for Gaussianity and Linearity of a Stationary Time Series. , 1982 .

[81]  G. Box,et al.  On a measure of lack of fit in time series models , 1978 .

[82]  F. Longin.  The Asymptotic Distribution of Extreme Stock Market Returns , 1996 .

[83]  H. Tong,et al.  ON ESTIMATING THRESHOLDS IN AUTOREGRESSIVE MODELS , 1986 .

[84]  M. Pourahmadi.  Joint mean-covariance models with applications to longitudinal data: Unconstrained parameterisation , 1999 .

[85]  G. C. Tiao,et al.  Usefulness of linear transformations in multivariate time-series analysis , 1993 .

[86]  Richard L. Smith.  Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone , 1989 .

[87]  D. M. Titterington,et al.  Neural Networks: A Review from a Statistical Perspective , 1994 .

[88]  Robert A. Wood,et al.  Market Microstructure Research Databases: History and Projections , 2000 .

[89]  J. Zakoian,et al.  Threshold Arch Models and Asymmetries in Volatility , 1993 .

[90]  Daniel Peña,et al.  Detection of outlier patches in autoregressive time series , 1998 .

[91]  A. Jenkinson.  The frequency distribution of the annual maximum (or minimum) values of meteorological elements , 1955 .

[92]  A. Lo,et al.  An Econometric Analysis of Nonsynchronous Trading , 1989 .

[93]  Richard L. Smith,et al.  Models for exceedances over high thresholds , 1990 .

[94]  R. Koenker,et al.  Computing regression quantiles , 1987 .

[95]  Charles M. Goldie,et al.  SLOW VARIATION WITH REMAINDER: THEORY AND APPLICATIONS , 1987 .

[96]  Bjørn Eraker.  MCMC Analysis of Diffusion Models With Application to Finance , 2001 .

[97]  K. Kroner,et al.  Arbitrage, Cointegration, and Testing the Unbiasedness Hypothesis in Financial Markets , 1995, Journal of Financial and Quantitative Analysis.

[98]  N. Shephard,et al.  Analysis of high dimensional multivariate stochastic volatility models , 2006 .

[99]  A. Lo.  Maximum Likelihood Estimation of Generalized Itô Processes with Discretely Sampled Data , 1986, Econometric Theory.

[100]  David S. Stoffer,et al.  Time series analysis and its applications , 2000 .

[101]  R. McCulloch,et al.  Bayesian Inference and Prediction for Mean and Variance Shifts in Autoregressive Time Series , 1993 .

[102]  B. Hansen,et al.  Inference in TAR Models , 1997 .

[103]  A. I. McLeod,et al.  Distribution of the Residual Autocorrelations in Multivariate Arma Time Series Models , 1981 .

[104]  R. Tsay,et al.  Outliers in multivariate time series , 2000 .

[105]  H. Tong.  On a threshold model , 1978 .

[106]  G. C. Tiao,et al.  Model Specification in Multivariate Time Series , 1989 .

[107]  William N. Venables,et al.  Modern Applied Statistics with S-Plus. , 1996 .

[108]  J. B. Ramsey,et al.  Tests for Specification Errors in Classical Linear Least‐Squares Regression Analysis , 1969 .

[109]  Richard A. Davis,et al.  Introduction to time series and forecasting , 1998 .

[110]  L. Haan,et al.  On the Estimation of the Extreme-Value Index and Large Quantile Estimation , 1989 .

[111]  Jianqing Fan,et al.  Functional-Coefficient Regression Models for Nonlinear Time Series , 2000 .

[112]  Jeffrey R. Russell,et al.  Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data , 1998 .

[113]  J. Pickands.  Statistical Inference Using Extreme Order Statistics , 1975 .

[114]  H. Kaiser.  The varimax criterion for analytic rotation in factor analysis , 1958 .

[115]  R. McCulloch,et al.  STATISTICAL ANALYSIS OF ECONOMIC TIME SERIES VIA MARKOV SWITCHING MODELS , 1994 .

[116]  A. I. McLeod,et al.  DIAGNOSTIC CHECKING ARMA TIME SERIES MODELS USING SQUARED‐RESIDUAL AUTOCORRELATIONS , 1983 .

[117]  G. Box,et al.  Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models , 1970 .

[118]  B. LeBaron,et al.  Nonlinear Dynamics, Chaos, and Instability: Statistical Theory and Economic Evidence , 1991 .

[119]  Peter S. Pande,et al.  The Six Sigma way , 2007 .

[120]  G. S. Watson,et al.  Smooth regression analysis , 1964 .

[121]  Peter R. Locke,et al.  Index arbitrage and nonlinear dynamics between the S&P 500 futures and cash , 1996 .

[122]  Yiu Kuen Tse,et al.  A test for constant correlations in a multivariate GARCH model , 2000 .

[123]  G. C. Tiao,et al.  The magnet effect of price limits: evidence from high-frequency data on Taiwan Stock Exchange , 2003 .

[124]  K. Chan,et al.  Percentage Points of Likelihood Ratio Tests for Threshold Autoregression , 1991 .

[125]  C. Z. Wei,et al.  Limiting Distributions of Least Squares Estimates of Unstable Autoregressive Processes , 1988 .

[126]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[127]  E. Nadaraya.  On Estimating Regression , 1964 .

[128]  Neil Shephard,et al.  Dynamics of Trade-by-Trade Price Movements: Decomposition and Models , 1999 .

[129]  Ruey S. Tsay,et al.  Functional-Coefficient Autoregressive Models , 1993 .

[130]  Gurdip Bakshi,et al.  Empirical Performance of Alternative Option Pricing Models , 1997 .

[131]  N. Shephard,et al.  Likelihood INference for Discretely Observed Non-linear Diffusions , 2001 .

[132]  Richard Schroeder,et al.  Six Sigma: The Breakthrough Management Strategy Revolutionizing the World's Top Corporations , 1999 .